There are also several books covering Python in depth. Pandas: powerful Python data analysis toolkit, Release 0. First you will need Conda to be installed and downloading and running the Miniconda will do this for you. The installer can be found here. Editors Overview of the interface and functionality of all editors. Modeling Meshes, curves, metaballs, text, modeling tools, and modifiers.
Grease Pencil 2D drawing and animation with Grease Pencil. Project links Homepage. Maintainers maximp. Follows PDF Tutorial and Documentation Tutorial, real-life examples and documentation. Related Projects pdfminer cool stuff pyPdf xpdf pdfbox mupdf.
Donation If this project is helpful, you can treat me to coffee Project details Project links Homepage. Download files Download the file for your platform. Files for pdfreader, version 0. Close Hashes for pdfreader File type Source. For example, the module name A. B designates a submodule named B in a package named A.
When importing the package, Python searches through the directories on sys. Users of the package can import individual modules from the package, for example: import sound. It must be referenced with its full name. Contrarily, when using syntax like import item. The only solution is for the package author to provide an explicit index of the package.
It is up to the package author to keep this list up-to-date when a new version of the package is released. It also includes any submodules of the package that were explicitly loaded by previous import statements. Consider this code: import sound. For example, if the module sound. You can also write relative imports, with the from module import name form of import statement. These imports use leading dots to indicate the current and parent packages involved in the relative import.
From the surround module for example, you might use: 50 Chapter 6. While this feature is not often needed, it can be used to extend the set of modules found in a package. This chapter will discuss some of the possibilities. See the Library Reference for more information on this. There are several ways to format output. The string type has some methods that perform useful operations for padding strings to a given column width. The str function is meant to return representations of values which are fairly human-readable, while repr is meant to generate representations which can be read by the interpreter or will force a SyntaxError if there is no equivalent syntax.
Many values, such as numbers or structures like lists and dictionaries, have the same representation using either function. Strings, in particular, have two distinct representations.
This allows greater control over how the value is formatted. This is useful for making columns line up. A number in the brackets can be used to refer to the position of the object passed into the str. Note use of 'end' on previous line The str. There are similar methods str. These methods do not write anything, they just return a new string.
If you really want truncation you can always add a slice operation, as in x. There is another method, str. It interprets the left argument much like a sprintf - style format string to be applied to the right argument, and returns the string resulting from this formatting operation.
For example: 56 Chapter 7. More information can be found in the old-string-formatting section. Otherwise, at most size bytes are read and returned. This makes the return value unambiguous; if f. This is the first line of the file. When you want to save more complex data types like nested lists and dictionaries, parsing and serializing by hand becomes complicated. The standard module called json can take Python data hierarchies, and convert them to string represen- tations; this process is called serializing.
Reconstructing the data from the string representation is called deserializing. Many programmers are already familiar with it, which makes it a good choice for interoperability. The reference for the json module contains an explanation of this. See also: pickle - the pickle module 7. It is also insecure by default: deserializing pickle data coming from an untrusted source can execute arbitrary code, if the data was crafted by a skilled attacker.
There are at least two distinguishable kinds of errors: syntax errors and exceptions. The error is caused by or at least detected at the token preceding the arrow: in the example, the error is detected at the function print , since a colon ':' is missing before it.
File name and line number are printed so you know where to look in case the input came from a script. Errors detected during execution are called exceptions and are not unconditionally fatal: you will soon learn how to handle them in Python programs. The string printed as the exception type is the name of the built-in exception that occurred. The rest of the line provides detail based on the type of exception and what caused it. The preceding part of the error message shows the context where the exception happened, in the form of a stack traceback.
In general it contains a stack traceback listing source lines; however, it will not display lines read from standard input. Look at the following example, which asks the user for input until a valid integer has been entered, but allows the user to interrupt the program using Control-C or whatever the operating system supports ; note that a user-generated interruption is signalled by raising the KeyboardInterrupt exception.
That was no valid number. Try again The try statement works as follows. Then if its type matches the exception named after the except keyword, the except clause is executed, and then execution continues after the try statement. At most one handler will be executed. Handlers only handle exceptions that occur in the corresponding try clause, not in other handlers of the same try statement.
An except clause may name multiple exceptions as a parenthesized tuple, for example For example, the following code will print B, C, D in that order: class B Exception : pass continues on next page 62 Chapter 8.
The last except clause may omit the exception name s , to serve as a wildcard. Use this with extreme caution, since it is easy to mask a real programming error in this way! It is useful for code that must be executed if the try clause does not raise an exception. For example: for arg in sys.
The presence and type of the argument depend on the exception type. The except clause may specify a variable after the exception name. The variable is bound to an exception instance with the arguments stored in instance. One may also 8.
Handling run-time error: division by zero 8. This must be either an exception instance or an exception class a class that derives from Exception. An exception flew by! Exceptions should typically be derived from the Exception class, either directly or indi- rectly. More information on classes is presented in chapter Classes. Goodbye, world! When an exception has occurred in the try clause and has not been handled by an except clause or it has occurred in an except or else clause , it is re-raised after the finally clause has been executed.
The TypeError raised by dividing two strings is not handled by the except clause and therefore re-raised after the finally clause has been executed. This is not an issue in simple scripts, but can be a problem for larger applications. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state.
Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name.
Objects can contain arbitrary amounts and kinds of data. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. This is known as aliasing in other languages. Incidentally, knowledge about this subject is useful for any advanced Python programmer.
A namespace is a mapping from names to objects. Examples of namespaces are: the set of built-in names containing functions such as abs , and built-in exception names ; the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace.
By the way, I use the word attribute for any name following a dot — for example, in the expression z. Strictly speaking, references to names in modules are attribute references: in the expression modname.
In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname. Writable attributes may also be deleted with the del statement. For example, del modname. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted.
The built-in names actually also live in a module; this is called builtins. The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. Actually, forgetting would be a better way to describe what actually happens. Of course, recursive invocations each have their own local namespace. A scope is a textual region of a Python program where a namespace is directly accessible.
Although scopes are determined statically, they are used dynamically. To rebind variables found outside of the innermost scope, the nonlocal statement can be used; if not declared nonlocal, those variables are read-only an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged. Obviously, using this violates the abstraction of namespace implementation, and should be restricted to things like post-mortem debuggers.
In fact, local variables are already determined statically. Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement del x removes the binding of x from the namespace referenced by the local scope. The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.
Attribute references use the standard syntax used for all attribute references in Python: obj. Class attributes can also be assigned to, so you can change the value of MyClass. Just pretend that the class object is a parameterless function that returns a new instance of the class.
The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names, data attributes and methods. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16, without leaving a trace: x.
In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on.
Valid method names of an instance object depend on its class. So in our example, x. But x. However, it is not necessary to call a method right away: x. What exactly happens when a method is called? You may have noticed that x. What happened to the argument? In our example, the call x. If the name denotes a valid class attribute that is a function object, a method object is created by packing pointers to the instance object and the function object just found together in an abstract object: this is the method object.
When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list. In other words, classes are not usable to implement pure abstract data types.
In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.
Clients should use data attributes with care — clients may mess up invariants maintained by the methods 9. There is no shorthand for referencing data attributes or other methods!
This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention. Note that this practice usually only serves to confuse the reader of a program. A class is never used as a global scope. Each value is an object, and therefore has a class also called its type.
It is stored as object. In place of a base class name, other arbitrary expressions are also allowed. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class.
This rule is applied recursively if the base class itself is derived from some other class. Method references are resolved as follows: the corresponding class attribute is searched, descending down the chain of base classes if necessary, and the method reference is valid if this yields a function object.
Derived classes may override methods of their base classes. An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call BaseClassName.
This is occasionally useful to clients as well. Note that this only works if the base class is accessible as BaseClassName in the global scope. However, issubclass float, int is False since float is not a subclass of int.
Thus, if an attribute is not found in DerivedClassName, it is searched for in Base1, then recursively in the base classes of Base1, and if it was not found there, it was searched for in Base2, and so on.
In fact, it is slightly more complex than that; the method resolution order changes dynamically to support cooperative calls to super. This approach is known in some other multiple-inheritance languages as call-next-method and is more powerful than the super call found in single-inheritance languages. Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond re- lationships where at least one of the parent classes can be accessed through multiple paths from the bottommost class.
For example, all classes inherit from object, so any case of multiple inheritance provides more than one path to reach object. Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. It should be considered an implementation detail and subject to change without notice. Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls.
This can even be useful in special circumstances, such as in the debugger. Instance method objects have attributes, too: m.
Behind the scenes, the for statement calls iter on the container object. They are written like regular functions but use the yield statement whenever they want to return data. An example shows that generators can be trivially easy to create: def reverse data : for index in range len data -1, -1, -1 : yield data[index] 80 Chapter 9. Another key feature is that the local variables and execution state are automatically saved between calls.
This made the function easier to write and much more clear than an approach using instance variables like self. In addition to automatic method creation and saving program state, when generators terminate, they au- tomatically raise StopIteration. These expressions are designed for situations where the gen- erator is used right away by an enclosing function.
This will keep os. For instance the following output results from running python demo. Two of the simplest are urllib. From: soothsayer example. Beware the Ides of March. The module also supports objects that are timezone aware. Python provides a measurement tool that answers those questions immediately. For example, it may be tempting to use the tuple packing and unpacking feature instead of the tradi- tional approach to swapping arguments. Test construction is as simple as cutting-and-pasting a typical call along with its results into the docstring.
This improves the documentation by providing the user with an example and it allows the doctest module to make sure the code remains true to the documentation: def average values : """Computes the arithmetic mean of a list of numbers. This is best seen through the sophisticated and robust capabilities of its larger packages. Despite the modules names, no direct knowledge or handling of XML is needed. Unlike smtplib and poplib which actually send and receive messages, the email package has a complete toolset for building or decoding complex message structures including attachments and for implementing internet encoding and header protocols.
XML processing is supported by the xml. ElementTree, xml. Together, these modules and packages greatly simplify data inter- change between Python applications and other tools. These mod- ules rarely occur in small scripts. This allows users to customize their applications without having to alter the application.
Surrounding the placeholder with braces allows it to be followed by more alphanumeric letters with no intervening spaces. Pack codes "H" and "I" represent two and four byte unsigned numbers respectively.
Threads can be used to improve the responsiveness of applications that accept user input while other tasks run in the background. The following code shows how the high level threading module can run tasks in background while the main program continues to run: import threading, zipfile class AsyncZip threading.
ZipFile self. To that end, the threading module provides a number of synchronization primitives including locks, events, condition variables, and semaphores. So, the preferred approach to task coordination is to concentrate all access to a resource in a single thread and then use the queue module to feed that thread with requests from other threads. Applications using Queue objects for inter-thread communication and coordination are easier to design, more readable, and more reliable.
Other output options include routing messages through email, datagrams, sockets, or to an HTTP Server. The memory is freed shortly after the last reference to it has been eliminated.
Unfortunately, just tracking them creates a reference that makes them permanent. The weakref module provides tools for tracking objects without creating a reference. When the object is no longer needed, it is automatically removed from a weakref table and a callback is triggered for weakref objects.
The array module provides an array object that is like a list that stores only homogeneous data and stores it more compactly. The lowest valued entry is always kept at position zero. This means it may not be possible for one Python installation to meet the requirements of every application. If application A needs version 1. The solution for this problem is to create a virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.
If application B requires a library be upgraded to version 3. If you use the csh or fish shells, there are alternate activate. Consult the installing- index guide for complete documentation for pip. A common convention is to put this list in a requirements.
Installing collected packages: novas, numpy, requests Running setup. Consult the installing-index guide for complete documentation for pip. Reading this tutorial has probably reinforced your interest in using Python — you should be eager to apply Python to solving your real-world problems. Where should you go to learn more? The standard Python distribution includes a lot of additional code. There are modules to read Unix mailboxes, retrieve documents via HTTP, generate random numbers, parse command-line options, write CGI programs, compress data, and many other tasks.
It contains code, documentation, and pointers to Python-related pages around the Web. This Web site is mirrored in various places around the world, such as Europe, Japan, and Australia; a mirror may be faster than the main site, depending on your geographical location.
For Python-related questions and problem reports, you can post to the newsgroup comp. The newsgroup and mailing list are gatewayed, so messages posted to one will automatically be forwarded to the other.
There are hundreds of postings a day, asking and answering questions, suggesting new features, and announcing new modules. The FAQ answers many of the questions that come up again and again, and may already contain the solution for your problem. What Now? This is implemented using the GNU Readline library, which supports various styles of editing. For dotted expressions such as string. The history will be available again during the next interactive interpreter session. A command to check or even suggest matching parentheses, quotes, etc.
One alternative enhanced interactive interpreter that has been around for quite some time is IPython, which features tab completion, object exploration and advanced history management.
It can also be thoroughly customized and embedded into other applications.
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